Search Results for author:
Found 11 papers, 3 papers with code
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs).
HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition
Large language models (LLMs) have emerged as a promising alternative to expensive human evaluations.
Text Diffusion with Reinforced Conditioning
Diffusion models have demonstrated exceptional capability in generating high-quality images, videos, and audio.
Improving Domain Adaptation through Extended-Text Reading Comprehension
To enhance the domain-specific capabilities of large language models, continued pre-training on a domain-specific corpus is a prevalent method.
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
In this paper, we propose a tailored learning approach to distill such reasoning ability to smaller LMs to facilitate the democratization of the exclusive reasoning ability.
Auto Search Indexer for End-to-End Document Retrieval
Generative retrieval, which is a new advanced paradigm for document retrieval, has recently attracted research interests, since it encodes all documents into the model and directly generates the retrieved documents.
Calibrating LLM-Based Evaluator
Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation quality, and a competent alternative to human evaluation.
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation
In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels and other noisy labels.
MMINR: Multi-frame-to-Multi-frame Inference with Noise Resistance for Precipitation Nowcasting with Radar
To address this problem, we propose a novel Multi-frame-to-Multi-frame Inference (MMI) model with Noise Resistance (NR) named MMINR.
Rainformer: Features Extraction Balanced Network for Radar-Based Precipitation Nowcasting
The experimental results show that Rainformer outperforms seven state of the arts methods on the benchmark database and provides more insights into the high-intensity rainfall prediction task.
Ranked #5 on
Weather Forecasting
on SEVIR
RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are widely used in many applications yet still need large amounts of labelled data for training.
